I decided to keep kristian-x-bookmarks as a local, script-driven archive with shared memory anchored in /Users/kristian/Documents/obsidian/agent; the current signals show no new architectural change, but they confirm the original direction is still the right operational baseline.

What We Built

• I have a Python-first bookmark pipeline organized around focused scripts (fetch.py, sync.py, digest.py, embed.py) with persistence in bookmarks.duckdb.
• The initial commit (c283812) sets the core intent clearly: “X bookmarks archive with vector search.”
• Day-to-day work appears concentrated in data flow and retrieval surfaces (fetch.py, sync.py, db.py, embed.py, search.py) rather than framework setup.
• Runtime entry is intentionally lightweight (run.sh, requirements.txt) with no package-script layer, which keeps operations direct for agent and human use.

Why We Built It

• The project optimizes for fast local operation and easy maintenance instead of service-heavy infrastructure.
• The top project rule is explicit: use the Obsidian agent vault as canonical shared memory when available, so decisions and context stay durable across sessions.
• Recent session intent (“when was the last time this was updated?”) highlights the practical need this repo serves: verifiable project recency and recoverable context, not just raw bookmark storage.
• Keeping the architecture scriptable and local reduces coordination overhead while still supporting semantic lookup (via embeddings + vector search intent from the initial commit).

How It Works

• The workflow is file-and-script oriented: fetch/update bookmark data, sync and digest it, generate embeddings, then query against local DuckDB-backed state.
• config.py and db.py provide operational glue so the scripts can run as a cohesive pipeline without a separate API service.
• The change evidence is implementation-focused (core Python modules plus bookmarks.duckdb), which suggests iterative tuning of ingestion/search behavior rather than product-surface expansion.
• Operationally, the next durable step is process discipline: follow the AGENTS guidance to prefetch QMD context before substantial work, then continue recording decisions as short, decision-first logbook entries.